GEV for single variable

FortNelson

maxEHF

## 
## Spearman correlation matrix
##      year   ab   fn   ke   pg   yv
## year 1.00 0.46 0.12 0.12 0.04 0.34
## ab   0.46 1.00 0.04 0.28 0.33 0.69
## fn   0.12 0.04 1.00 0.13 0.09 0.08
## ke   0.12 0.28 0.13 1.00 0.39 0.34
## pg   0.04 0.33 0.09 0.39 1.00 0.49
## yv   0.34 0.69 0.08 0.34 0.49 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 829.6514 
## 
## Estimates
##     loc    scale    shape  
## 25.0600  22.8921   0.3011  
## 
## Standard Errors
##    loc   scale   shape  
## 2.9647  2.5235  0.1194  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 27 
##   Gradient Evaluations: 15 
## 
## 
## Tail behavior based on shape parameter (0.301): Heavy-tailed

avgexc

## 
## Spearman correlation matrix
##      year    ab    fn   ke   pg   yv
## year 1.00  0.18  0.19 0.34 0.02 0.16
## ab   0.18  1.00 -0.04 0.20 0.43 0.59
## fn   0.19 -0.04  1.00 0.07 0.16 0.07
## ke   0.34  0.20  0.07 1.00 0.24 0.26
## pg   0.02  0.43  0.16 0.24 1.00 0.33
## yv   0.16  0.59  0.07 0.26 0.33 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 378.6132 
## 
## Estimates
##     loc    scale    shape  
##  4.5968   2.1678  -0.2201  
## 
## Standard Errors
##     loc    scale    shape  
## 0.25992  0.18320  0.06957  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 35 
##   Gradient Evaluations: 13 
## 
## 
## Tail behavior based on shape parameter (-0.22): Short-tailed

YVR

Assess trend for EHF

maxEHF

## 
## Spearman correlation matrix
##      year   ab   fn   ke   pg   yv
## year 1.00 0.46 0.12 0.12 0.04 0.34
## ab   0.46 1.00 0.04 0.28 0.33 0.69
## fn   0.12 0.04 1.00 0.13 0.09 0.08
## ke   0.12 0.28 0.13 1.00 0.39 0.34
## pg   0.04 0.33 0.09 0.39 1.00 0.49
## yv   0.34 0.69 0.08 0.34 0.49 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 576.283 
## 
## Estimates
##    loc   scale   shape  
## 6.9468  5.5791  0.1645  
## 
## Standard Errors
##     loc    scale    shape  
## 0.67927  0.52708  0.08107  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 33 
##   Gradient Evaluations: 11 
## 
## 
## Tail behavior based on shape parameter (0.164): Heavy-tailed

avgexc

## 
## Spearman correlation matrix
##      year    ab    fn   ke   pg   yv
## year 1.00  0.18  0.19 0.34 0.02 0.16
## ab   0.18  1.00 -0.04 0.20 0.43 0.59
## fn   0.19 -0.04  1.00 0.07 0.16 0.07
## ke   0.34  0.20  0.07 1.00 0.24 0.26
## pg   0.02  0.43  0.16 0.24 1.00 0.33
## yv   0.16  0.59  0.07 0.26 0.33 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 286.0057 
## 
## Estimates
##    loc   scale   shape  
##  2.138   1.286  -0.215  
## 
## Standard Errors
##     loc    scale    shape  
## 0.15040  0.09905  0.04432  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 50 
##   Gradient Evaluations: 12 
## 
## 
## Tail behavior based on shape parameter (-0.215): Short-tailed

Kelowna

maxEHF

## 
## Spearman correlation matrix
##      year   ab   fn   ke   pg   yv
## year 1.00 0.46 0.12 0.12 0.04 0.34
## ab   0.46 1.00 0.04 0.28 0.33 0.69
## fn   0.12 0.04 1.00 0.13 0.09 0.08
## ke   0.12 0.28 0.13 1.00 0.39 0.34
## pg   0.04 0.33 0.09 0.39 1.00 0.49
## yv   0.34 0.69 0.08 0.34 0.49 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 644.2863 
## 
## Estimates
##   loc  scale  shape  
## 8.948  8.391  0.153  
## 
## Standard Errors
##     loc    scale    shape  
## 1.03278  0.80137  0.08555  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 28 
##   Gradient Evaluations: 10 
## 
## 
## Tail behavior based on shape parameter (0.153): Heavy-tailed

avgexc

## 
## Spearman correlation matrix
##      year    ab    fn   ke   pg   yv
## year 1.00  0.18  0.19 0.34 0.02 0.16
## ab   0.18  1.00 -0.04 0.20 0.43 0.59
## fn   0.19 -0.04  1.00 0.07 0.16 0.07
## ke   0.34  0.20  0.07 1.00 0.24 0.26
## pg   0.02  0.43  0.16 0.24 1.00 0.33
## yv   0.16  0.59  0.07 0.26 0.33 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 292.314 
## 
## Estimates
##     loc    scale    shape  
##  2.3826   1.3209  -0.2067  
## 
## Standard Errors
##     loc    scale    shape  
## 0.15470  0.10314  0.04652  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 44 
##   Gradient Evaluations: 11 
## 
## 
## Tail behavior based on shape parameter (-0.207): Short-tailed

Abbotsford

maxEHF

## 
## Spearman correlation matrix
##      year   ab   fn   ke   pg   yv
## year 1.00 0.46 0.12 0.12 0.04 0.34
## ab   0.46 1.00 0.04 0.28 0.33 0.69
## fn   0.12 0.04 1.00 0.13 0.09 0.08
## ke   0.12 0.28 0.13 1.00 0.39 0.34
## pg   0.04 0.33 0.09 0.39 1.00 0.49
## yv   0.34 0.69 0.08 0.34 0.49 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 689.0903 
## 
## Estimates
##     loc    scale    shape  
## 12.9259  10.1461   0.2749  
## 
## Standard Errors
##     loc    scale    shape  
## 1.24486  1.03254  0.09072  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 28 
##   Gradient Evaluations: 12 
## 
## 
## Tail behavior based on shape parameter (0.275): Heavy-tailed

avgexc

## 
## Spearman correlation matrix
##      year    ab    fn   ke   pg   yv
## year 1.00  0.18  0.19 0.34 0.02 0.16
## ab   0.18  1.00 -0.04 0.20 0.43 0.59
## fn   0.19 -0.04  1.00 0.07 0.16 0.07
## ke   0.34  0.20  0.07 1.00 0.24 0.26
## pg   0.02  0.43  0.16 0.24 1.00 0.33
## yv   0.16  0.59  0.07 0.26 0.33 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 305.6872 
## 
## Estimates
##     loc    scale    shape  
##  3.6098   1.4094  -0.1845  
## 
## Standard Errors
##     loc    scale    shape  
## 0.16484  0.10960  0.04615  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 37 
##   Gradient Evaluations: 13 
## 
## 
## Tail behavior based on shape parameter (-0.184): Short-tailed

Prince George

maxEHF

## 
## Spearman correlation matrix
##      year   ab   fn   ke   pg   yv
## year 1.00 0.46 0.12 0.12 0.04 0.34
## ab   0.46 1.00 0.04 0.28 0.33 0.69
## fn   0.12 0.04 1.00 0.13 0.09 0.08
## ke   0.12 0.28 0.13 1.00 0.39 0.34
## pg   0.04 0.33 0.09 0.39 1.00 0.49
## yv   0.34 0.69 0.08 0.34 0.49 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 703.4146 
## 
## Estimates
##    loc   scale   shape  
## 13.553  12.148   0.107  
## 
## Standard Errors
##   loc  scale  shape  
## 1.535  1.185  0.100  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 21 
##   Gradient Evaluations: 7 
## 
## 
## Tail behavior based on shape parameter (0.107): Heavy-tailed

avgexc

## 
## Spearman correlation matrix
##      year    ab    fn   ke   pg   yv
## year 1.00  0.18  0.19 0.34 0.02 0.16
## ab   0.18  1.00 -0.04 0.20 0.43 0.59
## fn   0.19 -0.04  1.00 0.07 0.16 0.07
## ke   0.34  0.20  0.07 1.00 0.24 0.26
## pg   0.02  0.43  0.16 0.24 1.00 0.33
## yv   0.16  0.59  0.07 0.26 0.33 1.00
## 
## Call: fgev(x = data[[station]]) 
## Deviance: 306.4789 
## 
## Estimates
##     loc    scale    shape  
##  3.0860   1.4730  -0.2591  
## 
## Standard Errors
##     loc    scale    shape  
## 0.17273  0.11570  0.05068  
## 
## Optimization Information
##   Convergence: successful 
##   Function Evaluations: 46 
##   Gradient Evaluations: 12 
## 
## 
## Tail behavior based on shape parameter (-0.259): Short-tailed